The decoding of convolutional codes with soft-input and soft-output values is often performed according to the principle of the symbol-by-symbol MAP algorithm (MAP=Maximum A posteriori Probability). The a posteriori probability for the decoded symbols is maximized subject to the received sequence. The symbol-by-symbol MAP decoding algorithm can be realized by the trellis diagram of the convolutional code when a forward and backward recursion is used. Both the forward recursion and the backward recursion are very similar to the Viterbi algorithm but for the recursion direction. The accumulated metrics calculated during the backward recursion are to be stored, because they are necessary in the forward recursion for calculating the soft-output values. The memory requirement for this is N.2.sup.L-1 words (in currently customary fixed point digital signal processors (DSP's) a word usually comprises 16 bits), where N is the block length and L the influence length of the convolutional code. Typical values for L lie in the range [5, . . . 7]. Already with moderate block lengths N of several hundred bits, this implies a large memory requirement which cannot be satisfied in currently available digital signal processors. In view of the backward recursion and the storage of the metrics, the algorithm is primarily suitable for signal sequences which have a block structure. The exact symbol-by-symbol MAP algorithm is basically unsuitable for fixed point DSP's, because the algorithm needs as soft-input values probabilities whose combinations in the algorithm (multiplication and addition) will rapidly lead to numerical problems. Therefore, a sub-optimum variant is to be used in currently available fixed point DSP's, which variant uses either logarithmic probabilities or so-called log-likelihood ratios as soft-input values while the combinations in the algorithm then consist of additions and maximization.